Overview

Dataset statistics

Number of variables24
Number of observations1053
Missing cells354
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory205.7 KiB
Average record size in memory200.0 B

Variable types

Text2
DateTime1
Categorical4
Numeric14
Boolean3

Alerts

Abdominal Circumference (cm) is highly overall correlated with Waist-to-Height RatioHigh correlation
BMI is highly overall correlated with CVD Risk Score and 1 other fieldsHigh correlation
CVD Risk Score is highly overall correlated with BMIHigh correlation
Estimated LDL (mg/dL) is highly overall correlated with Total Cholesterol (mg/dL)High correlation
Height (cm) is highly overall correlated with Height (m)High correlation
Height (m) is highly overall correlated with Height (cm)High correlation
Total Cholesterol (mg/dL) is highly overall correlated with Estimated LDL (mg/dL)High correlation
Waist-to-Height Ratio is highly overall correlated with Abdominal Circumference (cm)High correlation
Weight (kg) is highly overall correlated with BMIHigh correlation
Weight (kg) has 45 (4.3%) missing valuesMissing
Height (m) has 37 (3.5%) missing valuesMissing
Abdominal Circumference (cm) has 41 (3.9%) missing valuesMissing
Total Cholesterol (mg/dL) has 41 (3.9%) missing valuesMissing
Height (cm) has 48 (4.6%) missing valuesMissing
Waist-to-Height Ratio has 53 (5.0%) missing valuesMissing
Systolic BP has 40 (3.8%) missing valuesMissing
Diastolic BP has 49 (4.7%) missing valuesMissing
Patient ID has unique valuesUnique

Reproduction

Analysis started2026-02-28 04:52:01.695547
Analysis finished2026-02-28 04:52:38.465998
Duration36.77 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Patient ID
Text

Unique 

Distinct1053
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
2026-02-27T23:52:38.788965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8424
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1053 ?
Unique (%)100.0%

Sample

1st rowisDx5313
2nd rowLHCK2961
3rd rowdCDO1109
4th rowpnpE1080
5th rowMQyB2747
ValueCountFrequency (%)
isdx53131
 
0.1%
lhck29611
 
0.1%
dcdo11091
 
0.1%
pnpe10801
 
0.1%
mqyb27471
 
0.1%
dhdn89681
 
0.1%
vkql97001
 
0.1%
nktq66891
 
0.1%
smmi39561
 
0.1%
alyl91881
 
0.1%
Other values (1043)1043
99.1%
2026-02-27T23:52:39.307612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9438
 
5.2%
7436
 
5.2%
0433
 
5.1%
8428
 
5.1%
1426
 
5.1%
2418
 
5.0%
6417
 
5.0%
3407
 
4.8%
5406
 
4.8%
4403
 
4.8%
Other values (52)4212
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)8424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9438
 
5.2%
7436
 
5.2%
0433
 
5.1%
8428
 
5.1%
1426
 
5.1%
2418
 
5.0%
6417
 
5.0%
3407
 
4.8%
5406
 
4.8%
4403
 
4.8%
Other values (52)4212
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9438
 
5.2%
7436
 
5.2%
0433
 
5.1%
8428
 
5.1%
1426
 
5.1%
2418
 
5.0%
6417
 
5.0%
3407
 
4.8%
5406
 
4.8%
4403
 
4.8%
Other values (52)4212
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9438
 
5.2%
7436
 
5.2%
0433
 
5.1%
8428
 
5.1%
1426
 
5.1%
2418
 
5.0%
6417
 
5.0%
3407
 
4.8%
5406
 
4.8%
4403
 
4.8%
Other values (52)4212
50.0%
Distinct818
Distinct (%)77.7%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
Minimum2020-01-02 00:00:00
Maximum2025-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-27T23:52:39.479178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:39.698478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
M
531 
F
522 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1053
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M531
50.4%
F522
49.6%

Length

2026-02-27T23:52:39.895283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-27T23:52:39.997148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m531
50.4%
f522
49.6%

Most occurring characters

ValueCountFrequency (%)
M531
50.4%
F522
49.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M531
50.4%
F522
49.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M531
50.4%
F522
49.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M531
50.4%
F522
49.6%

Age
Real number (ℝ)

Distinct57
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.05375
Minimum25
Maximum89.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2026-02-27T23:52:40.137680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile30
Q137
median46
Q356
95-th percentile72
Maximum89.42
Range64.42
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.620165
Coefficient of variation (CV)0.26820742
Kurtosis-0.26213384
Mean47.05375
Median Absolute Deviation (MAD)9
Skewness0.52687664
Sum49547.599
Variance159.26856
MonotonicityNot monotonic
2026-02-27T23:52:40.308928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3236
 
3.4%
4935
 
3.3%
4835
 
3.3%
3735
 
3.3%
3133
 
3.1%
3033
 
3.1%
3333
 
3.1%
4332
 
3.0%
3832
 
3.0%
5531
 
2.9%
Other values (47)718
68.2%
ValueCountFrequency (%)
257
 
0.7%
268
 
0.8%
276
 
0.6%
286
 
0.6%
299
 
0.9%
3033
3.1%
3133
3.1%
3236
3.4%
3333
3.1%
3429
2.8%
ValueCountFrequency (%)
89.421
 
0.1%
88.4641
 
0.1%
85.7151
 
0.1%
799
0.9%
784
 
0.4%
769
0.9%
7512
1.1%
744
 
0.4%
735
0.5%
728
0.8%

Weight (kg)
Real number (ℝ)

High correlation  Missing 

Distinct763
Distinct (%)75.7%
Missing45
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean86.227927
Minimum13.261
Maximum158.523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2026-02-27T23:52:40.480447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13.261
5-th percentile53.1
Q166.62175
median87.572
Q3105.6075
95-th percentile117.1965
Maximum158.523
Range145.262
Interquartile range (IQR)38.98575

Descriptive statistics

Standard deviation22.072743
Coefficient of variation (CV)0.25598137
Kurtosis-0.81630163
Mean86.227927
Median Absolute Deviation (MAD)19.828
Skewness-0.11242642
Sum86917.75
Variance487.20596
MonotonicityNot monotonic
2026-02-27T23:52:40.658090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116.26
 
0.6%
100.45
 
0.5%
95.74
 
0.4%
116.54
 
0.4%
110.54
 
0.4%
83.64
 
0.4%
99.54
 
0.4%
97.83
 
0.3%
1063
 
0.3%
73.33
 
0.3%
Other values (753)968
91.9%
(Missing)45
 
4.3%
ValueCountFrequency (%)
13.2611
 
0.1%
15.0361
 
0.1%
19.5781
 
0.1%
21.0381
 
0.1%
21.3161
 
0.1%
50.12
0.2%
50.21
 
0.1%
50.3071
 
0.1%
50.3431
 
0.1%
50.43
0.3%
ValueCountFrequency (%)
158.5231
0.1%
157.1641
0.1%
149.8771
0.1%
1201
0.1%
119.91
0.1%
119.81
0.1%
119.71
0.1%
119.61
0.1%
119.5711
0.1%
119.5171
0.1%

Height (m)
Real number (ℝ)

High correlation  Missing 

Distinct270
Distinct (%)26.6%
Missing37
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean1.7544833
Minimum1.38
Maximum2.146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2026-02-27T23:52:40.865026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.38
5-th percentile1.576
Q11.67
median1.7545
Q31.84
95-th percentile1.94025
Maximum2.146
Range0.766
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.11536958
Coefficient of variation (CV)0.065757013
Kurtosis-0.15935352
Mean1.7544833
Median Absolute Deviation (MAD)0.0855
Skewness0.033367479
Sum1782.555
Variance0.01331014
MonotonicityNot monotonic
2026-02-27T23:52:41.054939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.7633
 
3.1%
1.8131
 
2.9%
1.6330
 
2.8%
1.728
 
2.7%
1.8727
 
2.6%
1.7427
 
2.6%
1.6626
 
2.5%
1.7326
 
2.5%
1.8924
 
2.3%
1.6924
 
2.3%
Other values (260)740
70.3%
(Missing)37
 
3.5%
ValueCountFrequency (%)
1.381
 
0.1%
1.3881
 
0.1%
1.411
 
0.1%
1.5031
 
0.1%
1.5051
 
0.1%
1.5062
0.2%
1.5072
0.2%
1.5081
 
0.1%
1.5091
 
0.1%
1.513
0.3%
ValueCountFrequency (%)
2.1461
0.1%
2.1411
0.1%
2.1391
0.1%
2.1171
0.1%
2.111
0.1%
21
0.1%
1.9982
0.2%
1.9951
0.1%
1.991
0.1%
1.9891
0.1%

BMI
Real number (ℝ)

High correlation 

Distinct623
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.700844
Minimum15
Maximum53.028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2026-02-27T23:52:41.239649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile17.8
Q122.6
median28.502
Q334.3
95-th percentile40.1
Maximum53.028
Range38.028
Interquartile range (IQR)11.7

Descriptive statistics

Standard deviation7.3762674
Coefficient of variation (CV)0.25700524
Kurtosis-0.60266221
Mean28.700844
Median Absolute Deviation (MAD)5.902
Skewness0.24371537
Sum30221.989
Variance54.40932
MonotonicityNot monotonic
2026-02-27T23:52:41.420503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.58
 
0.8%
33.48
 
0.8%
206
 
0.6%
27.16
 
0.6%
35.66
 
0.6%
18.46
 
0.6%
33.86
 
0.6%
31.86
 
0.6%
20.86
 
0.6%
31.26
 
0.6%
Other values (613)989
93.9%
ValueCountFrequency (%)
152
0.2%
15.11
 
0.1%
15.31
 
0.1%
15.42
0.2%
15.51
 
0.1%
15.61
 
0.1%
15.72
0.2%
15.82
0.2%
163
0.3%
16.21
 
0.1%
ValueCountFrequency (%)
53.0281
0.1%
52.741
0.1%
52.1921
0.1%
52.1361
0.1%
51.9841
0.1%
51.0221
0.1%
46.21
0.1%
46.11
0.1%
45.61
0.1%
44.81
0.1%

Abdominal Circumference (cm)
Real number (ℝ)

High correlation  Missing 

Distinct660
Distinct (%)65.2%
Missing41
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean92.055434
Minimum49.542
Maximum136.336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2026-02-27T23:52:41.618242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum49.542
5-th percentile72.2595
Q180.592
median91.6
Q3102.61125
95-th percentile112.64305
Maximum136.336
Range86.794
Interquartile range (IQR)22.01925

Descriptive statistics

Standard deviation13.390628
Coefficient of variation (CV)0.14546266
Kurtosis-0.5456442
Mean92.055434
Median Absolute Deviation (MAD)11.009
Skewness0.23797259
Sum93160.099
Variance179.30893
MonotonicityNot monotonic
2026-02-27T23:52:41.790456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.36
 
0.6%
96.66
 
0.6%
99.16
 
0.6%
916
 
0.6%
101.16
 
0.6%
78.15
 
0.5%
74.25
 
0.5%
94.45
 
0.5%
75.15
 
0.5%
107.35
 
0.5%
Other values (650)957
90.9%
(Missing)41
 
3.9%
ValueCountFrequency (%)
49.5421
0.1%
702
0.2%
70.0911
0.1%
70.12
0.2%
70.1841
0.1%
70.21
0.1%
70.32
0.2%
70.41
0.1%
70.4111
0.1%
70.52
0.2%
ValueCountFrequency (%)
136.3361
0.1%
136.3191
0.1%
134.2971
0.1%
133.8461
0.1%
133.7351
0.1%
133.0651
0.1%
132.8611
0.1%
119.9961
0.1%
119.8741
0.1%
119.7361
0.1%
Distinct922
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
2026-02-27T23:52:42.146084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6
Min length5

Characters and Unicode

Total characters6318
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique805 ?
Unique (%)76.4%

Sample

1st row112/83
2nd row101/91
3rd row92/89
4th row121/68
5th row107/61
ValueCountFrequency (%)
114/634
 
0.4%
126/863
 
0.3%
111/973
 
0.3%
120/893
 
0.3%
129/613
 
0.3%
143/673
 
0.3%
121/683
 
0.3%
127/773
 
0.3%
127/843
 
0.3%
139/813
 
0.3%
Other values (912)1022
97.1%
2026-02-27T23:52:42.617763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11521
24.1%
/1053
16.7%
9566
 
9.0%
6495
 
7.8%
7473
 
7.5%
0429
 
6.8%
8420
 
6.6%
2370
 
5.9%
3364
 
5.8%
4355
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)6318
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11521
24.1%
/1053
16.7%
9566
 
9.0%
6495
 
7.8%
7473
 
7.5%
0429
 
6.8%
8420
 
6.6%
2370
 
5.9%
3364
 
5.8%
4355
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6318
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11521
24.1%
/1053
16.7%
9566
 
9.0%
6495
 
7.8%
7473
 
7.5%
0429
 
6.8%
8420
 
6.6%
2370
 
5.9%
3364
 
5.8%
4355
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6318
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11521
24.1%
/1053
16.7%
9566
 
9.0%
6495
 
7.8%
7473
 
7.5%
0429
 
6.8%
8420
 
6.6%
2370
 
5.9%
3364
 
5.8%
4355
 
5.6%

Total Cholesterol (mg/dL)
Real number (ℝ)

High correlation  Missing 

Distinct208
Distinct (%)20.6%
Missing41
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean198.60713
Minimum-1.256
Maximum385.679
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size16.5 KiB
2026-02-27T23:52:42.780646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.256
5-th percentile109
Q1151.75
median198
Q3250
95-th percentile290
Maximum385.679
Range386.935
Interquartile range (IQR)98.25

Descriptive statistics

Standard deviation58.732233
Coefficient of variation (CV)0.29572066
Kurtosis-0.6550607
Mean198.60713
Median Absolute Deviation (MAD)48.5
Skewness-0.075735065
Sum200990.42
Variance3449.4752
MonotonicityNot monotonic
2026-02-27T23:52:42.990648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16011
 
1.0%
17910
 
0.9%
10910
 
0.9%
14710
 
0.9%
1509
 
0.9%
1929
 
0.9%
1669
 
0.9%
1939
 
0.9%
2959
 
0.9%
2618
 
0.8%
Other values (198)918
87.2%
(Missing)41
 
3.9%
ValueCountFrequency (%)
-1.2561
 
0.1%
1.8171
 
0.1%
8.4981
 
0.1%
16.0881
 
0.1%
19.9321
 
0.1%
21.6621
 
0.1%
1006
0.6%
1014
0.4%
1022
 
0.2%
1034
0.4%
ValueCountFrequency (%)
385.6791
 
0.1%
3004
0.4%
2994
0.4%
2984
0.4%
2974
0.4%
2967
0.7%
2959
0.9%
2945
0.5%
2935
0.5%
2922
 
0.2%

HDL (mg/dL)
Real number (ℝ)

Distinct69
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.929754
Minimum0.008
Maximum110.315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2026-02-27T23:52:43.177633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile32
Q142
median56
Q369
95-th percentile81
Maximum110.315
Range110.307
Interquartile range (IQR)27

Descriptive statistics

Standard deviation16.488026
Coefficient of variation (CV)0.29479882
Kurtosis-0.53287547
Mean55.929754
Median Absolute Deviation (MAD)14
Skewness0.0037753676
Sum58894.031
Variance271.85499
MonotonicityNot monotonic
2026-02-27T23:52:43.363924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4628
 
2.7%
3827
 
2.6%
6326
 
2.5%
7825
 
2.4%
5924
 
2.3%
4424
 
2.3%
6123
 
2.2%
3123
 
2.2%
7623
 
2.2%
3622
 
2.1%
Other values (59)808
76.7%
ValueCountFrequency (%)
0.0081
 
0.1%
0.6121
 
0.1%
1.2761
 
0.1%
6.2831
 
0.1%
6.8091
 
0.1%
7.5421
 
0.1%
3022
2.1%
3123
2.2%
3220
1.9%
3317
1.6%
ValueCountFrequency (%)
110.3151
 
0.1%
108.3041
 
0.1%
104.8821
 
0.1%
897
0.7%
883
 
0.3%
876
0.6%
864
0.4%
855
0.5%
845
0.5%
838
0.8%

Fasting Blood Sugar (mg/dL)
Real number (ℝ)

Distinct140
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.2302
Minimum15.306
Maximum219.667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2026-02-27T23:52:43.539227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15.306
5-th percentile73
Q191
median114
Q3137
95-th percentile176
Maximum219.667
Range204.361
Interquartile range (IQR)46

Descriptive statistics

Standard deviation32.044551
Coefficient of variation (CV)0.27569901
Kurtosis0.19749028
Mean116.2302
Median Absolute Deviation (MAD)23
Skewness0.47022498
Sum122390.4
Variance1026.8532
MonotonicityNot monotonic
2026-02-27T23:52:43.745195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7019
 
1.8%
9017
 
1.6%
9617
 
1.6%
11517
 
1.6%
8917
 
1.6%
7516
 
1.5%
14816
 
1.5%
10716
 
1.5%
9316
 
1.5%
12516
 
1.5%
Other values (130)886
84.1%
ValueCountFrequency (%)
15.3061
 
0.1%
15.6051
 
0.1%
18.961
 
0.1%
19.0141
 
0.1%
21.2111
 
0.1%
23.8171
 
0.1%
7019
1.8%
716
 
0.6%
729
0.9%
7314
1.3%
ValueCountFrequency (%)
219.6671
 
0.1%
219.1351
 
0.1%
218.0191
 
0.1%
215.6141
 
0.1%
213.6851
 
0.1%
212.9841
 
0.1%
212.3821
 
0.1%
1983
0.3%
1971
 
0.1%
1962
0.2%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
True
543 
False
510 
ValueCountFrequency (%)
True543
51.6%
False510
48.4%
2026-02-27T23:52:43.878379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
True
538 
False
515 
ValueCountFrequency (%)
True538
51.1%
False515
48.9%
2026-02-27T23:52:43.946038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
High
365 
Moderate
346 
Low
342 

Length

Max length8
Median length4
Mean length4.9895537
Min length3

Characters and Unicode

Total characters5254
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowModerate
4th rowLow
5th rowHigh

Common Values

ValueCountFrequency (%)
High365
34.7%
Moderate346
32.9%
Low342
32.5%

Length

2026-02-27T23:52:44.062250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-27T23:52:44.179059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high365
34.7%
moderate346
32.9%
low342
32.5%

Most occurring characters

ValueCountFrequency (%)
e692
13.2%
o688
13.1%
g365
 
6.9%
i365
 
6.9%
H365
 
6.9%
h365
 
6.9%
M346
 
6.6%
d346
 
6.6%
r346
 
6.6%
a346
 
6.6%
Other values (3)1030
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)5254
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e692
13.2%
o688
13.1%
g365
 
6.9%
i365
 
6.9%
H365
 
6.9%
h365
 
6.9%
M346
 
6.6%
d346
 
6.6%
r346
 
6.6%
a346
 
6.6%
Other values (3)1030
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5254
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e692
13.2%
o688
13.1%
g365
 
6.9%
i365
 
6.9%
H365
 
6.9%
h365
 
6.9%
M346
 
6.6%
d346
 
6.6%
r346
 
6.6%
a346
 
6.6%
Other values (3)1030
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5254
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e692
13.2%
o688
13.1%
g365
 
6.9%
i365
 
6.9%
H365
 
6.9%
h365
 
6.9%
M346
 
6.6%
d346
 
6.6%
r346
 
6.6%
a346
 
6.6%
Other values (3)1030
19.6%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
True
533 
False
520 
ValueCountFrequency (%)
True533
50.6%
False520
49.4%
2026-02-27T23:52:44.268138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Height (cm)
Real number (ℝ)

High correlation  Missing 

Distinct380
Distinct (%)37.8%
Missing48
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean175.50485
Minimum136.498
Maximum214.394
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2026-02-27T23:52:44.395783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum136.498
5-th percentile157.827
Q1167
median175.51
Q3184
95-th percentile193.9866
Maximum214.394
Range77.896
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.408675
Coefficient of variation (CV)0.065004899
Kurtosis-0.19261863
Mean175.50485
Median Absolute Deviation (MAD)8.51
Skewness0.042303485
Sum176382.38
Variance130.15787
MonotonicityNot monotonic
2026-02-27T23:52:44.600466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17632
 
3.0%
18129
 
2.8%
17029
 
2.8%
16328
 
2.7%
17427
 
2.6%
16626
 
2.5%
18726
 
2.5%
18323
 
2.2%
18623
 
2.2%
17323
 
2.2%
Other values (370)739
70.2%
(Missing)48
 
4.6%
ValueCountFrequency (%)
136.4981
0.1%
141.4231
0.1%
1501
0.1%
150.2831
0.1%
150.6081
0.1%
150.6161
0.1%
150.7091
0.1%
150.7211
0.1%
150.811
0.1%
150.8681
0.1%
ValueCountFrequency (%)
214.3941
0.1%
213.921
0.1%
211.1271
0.1%
210.9811
0.1%
210.5541
0.1%
199.961
0.1%
199.8211
0.1%
199.8021
0.1%
199.5481
0.1%
198.9661
0.1%

Waist-to-Height Ratio
Real number (ℝ)

High correlation  Missing 

Distinct325
Distinct (%)32.5%
Missing53
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean0.526384
Minimum0.26
Maximum0.804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2026-02-27T23:52:44.794243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.26
5-th percentile0.403
Q10.456
median0.523
Q30.585
95-th percentile0.66705
Maximum0.804
Range0.544
Interquartile range (IQR)0.129

Descriptive statistics

Standard deviation0.085569601
Coefficient of variation (CV)0.16256117
Kurtosis-0.14537114
Mean0.526384
Median Absolute Deviation (MAD)0.063
Skewness0.3574808
Sum526.384
Variance0.0073221567
MonotonicityNot monotonic
2026-02-27T23:52:44.984370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.42311
 
1.0%
0.4999
 
0.9%
0.4129
 
0.9%
0.4758
 
0.8%
0.5788
 
0.8%
0.5528
 
0.8%
0.5228
 
0.8%
0.5188
 
0.8%
0.5618
 
0.8%
0.4348
 
0.8%
Other values (315)915
86.9%
(Missing)53
 
5.0%
ValueCountFrequency (%)
0.261
0.1%
0.2671
0.1%
0.361
0.1%
0.3621
0.1%
0.3651
0.1%
0.3661
0.1%
0.371
0.1%
0.3741
0.1%
0.3762
0.2%
0.3791
0.1%
ValueCountFrequency (%)
0.8042
0.2%
0.7872
0.2%
0.7851
0.1%
0.7841
0.1%
0.7831
0.1%
0.7821
0.1%
0.7591
0.1%
0.7551
0.1%
0.7491
0.1%
0.7391
0.1%

Systolic BP
Real number (ℝ)

Missing 

Distinct98
Distinct (%)9.7%
Missing40
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean126.03415
Minimum49.914
Maximum202.711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2026-02-27T23:52:45.157937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum49.914
5-th percentile93
Q1108
median125
Q3142
95-th percentile168.4
Maximum202.711
Range152.797
Interquartile range (IQR)34

Descriptive statistics

Standard deviation22.891691
Coefficient of variation (CV)0.18163085
Kurtosis-0.16626081
Mean126.03415
Median Absolute Deviation (MAD)17
Skewness0.29606008
Sum127672.6
Variance524.0295
MonotonicityNot monotonic
2026-02-27T23:52:45.360221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13924
 
2.3%
10222
 
2.1%
11122
 
2.1%
12519
 
1.8%
13619
 
1.8%
11319
 
1.8%
14419
 
1.8%
12718
 
1.7%
11918
 
1.7%
10118
 
1.7%
Other values (88)815
77.4%
(Missing)40
 
3.8%
ValueCountFrequency (%)
49.9141
 
0.1%
51.1481
 
0.1%
53.2381
 
0.1%
58.5131
 
0.1%
9010
0.9%
9115
1.4%
9214
1.3%
9314
1.3%
9412
1.1%
9516
1.5%
ValueCountFrequency (%)
202.7111
 
0.1%
197.4991
 
0.1%
194.1731
 
0.1%
192.4011
 
0.1%
1795
0.5%
1787
0.7%
1774
0.4%
1763
0.3%
1754
0.4%
1744
0.4%

Diastolic BP
Real number (ℝ)

Missing 

Distinct72
Distinct (%)7.2%
Missing49
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean82.922879
Minimum34.047
Maximum134.066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2026-02-27T23:52:45.546646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum34.047
5-th percentile61
Q170
median82
Q394
95-th percentile113
Maximum134.066
Range100.019
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.787561
Coefficient of variation (CV)0.19038848
Kurtosis0.052727872
Mean82.922879
Median Absolute Deviation (MAD)12
Skewness0.33840004
Sum83254.571
Variance249.24708
MonotonicityNot monotonic
2026-02-27T23:52:45.723739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9128
 
2.7%
6127
 
2.6%
6527
 
2.6%
7826
 
2.5%
9226
 
2.5%
6326
 
2.5%
6626
 
2.5%
8425
 
2.4%
7125
 
2.4%
6424
 
2.3%
Other values (62)744
70.7%
(Missing)49
 
4.7%
ValueCountFrequency (%)
34.0471
 
0.1%
35.2432
 
0.2%
35.3171
 
0.1%
35.7931
 
0.1%
36.9951
 
0.1%
37.6021
 
0.1%
6022
2.1%
6127
2.6%
6215
1.4%
6326
2.5%
ValueCountFrequency (%)
134.0661
 
0.1%
133.8311
 
0.1%
132.1422
 
0.2%
131.561
 
0.1%
131.4251
 
0.1%
128.1651
 
0.1%
1197
0.7%
1186
0.6%
1174
0.4%
1164
0.4%
Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
Hypertension Stage 2
441 
Hypertension Stage 1
348 
Normal
192 
Elevated
72 

Length

Max length20
Median length20
Mean length16.626781
Min length6

Characters and Unicode

Total characters17508
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHypertension Stage 1
2nd rowHypertension Stage 2
3rd rowHypertension Stage 1
4th rowElevated
5th rowNormal

Common Values

ValueCountFrequency (%)
Hypertension Stage 2441
41.9%
Hypertension Stage 1348
33.0%
Normal192
18.2%
Elevated72
 
6.8%

Length

2026-02-27T23:52:45.890374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-27T23:52:45.995337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hypertension789
30.0%
stage789
30.0%
2441
16.8%
1348
13.2%
normal192
 
7.3%
elevated72
 
2.7%

Most occurring characters

ValueCountFrequency (%)
e2511
14.3%
t1650
 
9.4%
1578
 
9.0%
n1578
 
9.0%
a1053
 
6.0%
r981
 
5.6%
o981
 
5.6%
p789
 
4.5%
H789
 
4.5%
y789
 
4.5%
Other values (12)4809
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)17508
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e2511
14.3%
t1650
 
9.4%
1578
 
9.0%
n1578
 
9.0%
a1053
 
6.0%
r981
 
5.6%
o981
 
5.6%
p789
 
4.5%
H789
 
4.5%
y789
 
4.5%
Other values (12)4809
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17508
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e2511
14.3%
t1650
 
9.4%
1578
 
9.0%
n1578
 
9.0%
a1053
 
6.0%
r981
 
5.6%
o981
 
5.6%
p789
 
4.5%
H789
 
4.5%
y789
 
4.5%
Other values (12)4809
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17508
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e2511
14.3%
t1650
 
9.4%
1578
 
9.0%
n1578
 
9.0%
a1053
 
6.0%
r981
 
5.6%
o981
 
5.6%
p789
 
4.5%
H789
 
4.5%
y789
 
4.5%
Other values (12)4809
27.5%

Estimated LDL (mg/dL)
Real number (ℝ)

High correlation 

Distinct231
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.49283
Minimum1
Maximum316.071
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2026-02-27T23:52:46.155566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28
Q164
median112
Q3162
95-th percentile207.4
Maximum316.071
Range315.071
Interquartile range (IQR)98

Descriptive statistics

Standard deviation59.623066
Coefficient of variation (CV)0.52075806
Kurtosis-0.55106302
Mean114.49283
Median Absolute Deviation (MAD)49
Skewness0.27062462
Sum120560.95
Variance3554.9099
MonotonicityNot monotonic
2026-02-27T23:52:46.346609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9013
 
1.2%
12412
 
1.1%
12512
 
1.1%
17812
 
1.1%
5811
 
1.0%
15111
 
1.0%
18811
 
1.0%
4311
 
1.0%
3910
 
0.9%
19310
 
0.9%
Other values (221)940
89.3%
ValueCountFrequency (%)
14
0.4%
62
0.2%
71
 
0.1%
81
 
0.1%
94
0.4%
102
0.2%
111
 
0.1%
123
0.3%
131
 
0.1%
141
 
0.1%
ValueCountFrequency (%)
316.0711
0.1%
311.2461
0.1%
308.5141
0.1%
306.9211
0.1%
300.2272
0.2%
298.4921
0.1%
292.2551
0.1%
2372
0.2%
2341
0.1%
2321
0.1%

CVD Risk Score
Real number (ℝ)

High correlation 

Distinct825
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.783187
Minimum10.424
Maximum99.775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 KiB
2026-02-27T23:52:46.535806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.424
5-th percentile13.1572
Q115.25
median16.964
Q318.84
95-th percentile21.3298
Maximum99.775
Range89.351
Interquartile range (IQR)3.59

Descriptive statistics

Standard deviation7.7532428
Coefficient of variation (CV)0.43598725
Kurtosis80.47732
Mean17.783187
Median Absolute Deviation (MAD)1.806
Skewness8.4323857
Sum18725.696
Variance60.112773
MonotonicityNot monotonic
2026-02-27T23:52:46.705277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.854
 
0.4%
16.334
 
0.4%
144
 
0.4%
17.054
 
0.4%
15.454
 
0.4%
17.74
 
0.4%
15.414
 
0.4%
14.164
 
0.4%
14.813
 
0.3%
16.313
 
0.3%
Other values (815)1015
96.4%
ValueCountFrequency (%)
10.4241
0.1%
10.531
0.1%
10.861
0.1%
10.891
0.1%
11.111
0.1%
11.252
0.2%
11.31
0.1%
11.61
0.1%
11.611
0.1%
11.6331
0.1%
ValueCountFrequency (%)
99.7751
0.1%
99.3711
0.1%
98.651
0.1%
96.8071
0.1%
95.6841
0.1%
94.6291
0.1%
93.8981
0.1%
91.3141
0.1%
56.9051
0.1%
55.8231
0.1%

CVD Risk Level
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
HIGH
503 
INTERMEDIARY
398 
LOW
152 

Length

Max length12
Median length4
Mean length6.8793922
Min length3

Characters and Unicode

Total characters7244
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHIGH
2nd rowINTERMEDIARY
3rd rowHIGH
4th rowHIGH
5th rowINTERMEDIARY

Common Values

ValueCountFrequency (%)
HIGH503
47.8%
INTERMEDIARY398
37.8%
LOW152
 
14.4%

Length

2026-02-27T23:52:46.863434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-27T23:52:46.965372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high503
47.8%
intermediary398
37.8%
low152
 
14.4%

Most occurring characters

ValueCountFrequency (%)
I1299
17.9%
H1006
13.9%
E796
11.0%
R796
11.0%
G503
 
6.9%
N398
 
5.5%
T398
 
5.5%
M398
 
5.5%
D398
 
5.5%
A398
 
5.5%
Other values (4)854
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)7244
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I1299
17.9%
H1006
13.9%
E796
11.0%
R796
11.0%
G503
 
6.9%
N398
 
5.5%
T398
 
5.5%
M398
 
5.5%
D398
 
5.5%
A398
 
5.5%
Other values (4)854
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7244
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I1299
17.9%
H1006
13.9%
E796
11.0%
R796
11.0%
G503
 
6.9%
N398
 
5.5%
T398
 
5.5%
M398
 
5.5%
D398
 
5.5%
A398
 
5.5%
Other values (4)854
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7244
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I1299
17.9%
H1006
13.9%
E796
11.0%
R796
11.0%
G503
 
6.9%
N398
 
5.5%
T398
 
5.5%
M398
 
5.5%
D398
 
5.5%
A398
 
5.5%
Other values (4)854
11.8%

Interactions

2026-02-27T23:52:35.479874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:04.294091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-27T23:52:19.709960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:21.801465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:24.135576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:26.366720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:28.606795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-27T23:52:08.570720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-27T23:52:19.406371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:21.502927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:23.794512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:26.069228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:28.286145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:30.514720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:32.643158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:35.183404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:37.387676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:08.135719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:10.324289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:13.000146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:15.138529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:17.262761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:19.561836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:21.649500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:23.982944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:26.224139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:28.455999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:30.690709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:32.788038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-27T23:52:35.331952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-27T23:52:47.119568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Abdominal Circumference (cm)AgeBMIBlood Pressure CategoryCVD Risk LevelCVD Risk ScoreDiabetes StatusDiastolic BPEstimated LDL (mg/dL)Family History of CVDFasting Blood Sugar (mg/dL)HDL (mg/dL)Height (cm)Height (m)Physical Activity LevelSexSmoking StatusSystolic BPTotal Cholesterol (mg/dL)Waist-to-Height RatioWeight (kg)
Abdominal Circumference (cm)1.0000.0640.0330.0680.0630.1060.1050.0400.0620.0000.012-0.024-0.031-0.0140.0530.0000.0000.0430.0520.8880.042
Age0.0641.0000.0280.0760.1650.0570.0450.0410.0050.0000.0930.0350.0170.0210.0000.0000.0490.0590.0180.0440.004
BMI0.0330.0281.0000.0710.1460.6160.0510.0640.0220.0000.043-0.007-0.167-0.1570.0000.0570.0350.0130.0220.0980.641
Blood Pressure Category0.0680.0760.0711.0000.0780.1180.0000.4190.0130.0000.0900.0340.0550.0590.0530.0000.0000.4470.0000.0400.000
CVD Risk Level0.0630.1650.1460.0781.0000.1180.1720.1110.1460.2080.1140.1420.1270.1320.1210.0000.2180.1030.1300.0360.108
CVD Risk Score0.1060.0570.6160.1180.1181.0000.3010.1170.4480.0000.0820.054-0.065-0.0590.0000.0000.0330.4160.4670.1290.428
Diabetes Status0.1050.0450.0510.0000.1720.3011.0000.0000.0500.0000.0080.0000.0000.0000.0520.0000.0000.0540.0380.0410.089
Diastolic BP0.0400.0410.0640.4190.1110.1170.0001.0000.1220.0000.0820.013-0.006-0.0020.0000.0240.0000.0210.1330.0260.038
Estimated LDL (mg/dL)0.0620.0050.0220.0130.1460.4480.0500.1221.0000.000-0.005-0.1510.0410.0520.0000.0000.0000.0110.9300.0350.019
Family History of CVD0.0000.0000.0000.0000.2080.0000.0000.0000.0001.0000.0000.0310.0290.0710.0000.0060.0000.0000.0000.0000.000
Fasting Blood Sugar (mg/dL)0.0120.0930.0430.0900.1140.0820.0080.082-0.0050.0001.0000.0670.0350.0440.0000.0840.0430.0450.002-0.0000.061
HDL (mg/dL)-0.0240.035-0.0070.0340.1420.0540.0000.013-0.1510.0310.0671.000-0.012-0.0250.0000.0460.0320.0690.105-0.0060.012
Height (cm)-0.0310.017-0.1670.0550.127-0.0650.000-0.0060.0410.0290.035-0.0121.0000.9790.0000.0000.0000.0410.039-0.3960.009
Height (m)-0.0140.021-0.1570.0590.132-0.0590.000-0.0020.0520.0710.044-0.0250.9791.0000.0000.0380.0000.0320.048-0.3900.011
Physical Activity Level0.0530.0000.0000.0530.1210.0000.0520.0000.0000.0000.0000.0000.0000.0001.0000.0000.0020.0000.0440.0300.000
Sex0.0000.0000.0570.0000.0000.0000.0000.0240.0000.0060.0840.0460.0000.0380.0001.0000.0350.0000.0000.0000.062
Smoking Status0.0000.0490.0350.0000.2180.0330.0000.0000.0000.0000.0430.0320.0000.0000.0020.0351.0000.0000.0000.0000.000
Systolic BP0.0430.0590.0130.4470.1030.4160.0540.0210.0110.0000.0450.0690.0410.0320.0000.0000.0001.0000.0210.0390.012
Total Cholesterol (mg/dL)0.0520.0180.0220.0000.1300.4670.0380.1330.9300.0000.0020.1050.0390.0480.0440.0000.0000.0211.0000.0310.023
Waist-to-Height Ratio0.8880.0440.0980.0400.0360.1290.0410.0260.0350.000-0.000-0.006-0.396-0.3900.0300.0000.0000.0390.0311.0000.037
Weight (kg)0.0420.0040.6410.0000.1080.4280.0890.0380.0190.0000.0610.0120.0090.0110.0000.0620.0000.0120.0230.0371.000

Missing values

2026-02-27T23:52:37.656520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-27T23:52:37.971004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-27T23:52:38.301768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Patient IDDate of ServiceSexAgeWeight (kg)Height (m)BMIAbdominal Circumference (cm)Blood Pressure (mmHg)Total Cholesterol (mg/dL)HDL (mg/dL)Fasting Blood Sugar (mg/dL)Smoking StatusDiabetes StatusPhysical Activity LevelFamily History of CVDHeight (cm)Waist-to-Height RatioSystolic BPDiastolic BPBlood Pressure CategoryEstimated LDL (mg/dL)CVD Risk ScoreCVD Risk Level
0isDx5313November 08, 2023M44.0114.3001.72038.600100.000112/83228.077.091.0YYHighN172.0000.581112.083.0Hypertension Stage 1121.019.880HIGH
1LHCK296120/03/2024F57.092.9231.84233.116106.315101/91158.071.076.0NYHighY184.1720.577101.091.0Hypertension Stage 257.016.833INTERMEDIARY
2dCDO1109April 18, 2022F35.0113.3001.78035.80079.60092/89158.034.0111.0YNModerateY178.0000.44792.089.0Hypertension Stage 194.014.920HIGH
3pnpE108001/11/2024F48.0102.2001.75033.400106.700121/68207.049.0147.0YYLowY175.0000.610121.068.0Elevated128.018.870HIGH
4MQyB274725 Mar 24M43.052.7001.85015.400107.700107/61105.032.070.0YNHighN185.0000.582107.061.0Normal43.010.530INTERMEDIARY
5DHdn896822 May 25F31.087.0001.66031.60091.500139/81207.056.082.0NNLowY166.0000.551139.081.0Hypertension Stage 1121.017.410HIGH
6vkQL9700October 26, 2023M69.059.6841.94023.914117.986106/115206.042.0140.0YYHighY193.9810.608106.0115.0Hypertension Stage 2134.016.203HIGH
7nktq6689January 16, 2022F57.0100.1301.84022.24280.814165/99123.054.094.0NNLowN183.9880.439165.099.0Hypertension Stage 239.015.158LOW
8SMmI395610/11/2023M43.0117.9001.90032.70093.100127/99293.069.0125.0NNModerateYNaN0.490127.099.0Hypertension Stage 2194.018.750INTERMEDIARY
9aLYL918805 Dec 21F58.0NaN1.75031.80071.400135/88272.058.0132.0NNLowN175.0000.408135.088.0Hypertension Stage 1184.018.550HIGH
Patient IDDate of ServiceSexAgeWeight (kg)Height (m)BMIAbdominal Circumference (cm)Blood Pressure (mmHg)Total Cholesterol (mg/dL)HDL (mg/dL)Fasting Blood Sugar (mg/dL)Smoking StatusDiabetes StatusPhysical Activity LevelFamily History of CVDHeight (cm)Waist-to-Height RatioSystolic BPDiastolic BPBlood Pressure CategoryEstimated LDL (mg/dL)CVD Risk ScoreCVD Risk Level
1129dJuC5084December 02, 2020M55.0106.0001.82032.000109.30097/63287.035.000130.000NYModerateN182.0000.60197.063.0Normal222.018.990HIGH
1130CDsa265123/06/2025M39.073.3001.74024.20095.000111/84158.037.00081.000NYHighY174.0000.546111.084.0Hypertension Stage 191.015.550INTERMEDIARY
1131LpyK726924/06/2024F26.057.2501.85929.97181.215111/76147.0110.315215.614YYHighN185.8640.437111.076.0Normal60.016.484HIGH
1133FTEC44462022-11-05F73.066.0742.14627.46176.599112/106197.055.000131.000NNLowY195.8200.391112.0106.0Hypertension Stage 2112.053.754INTERMEDIARY
1137nmgP2712October 15, 2022M44.097.8001.89027.40091.900129/61166.061.000143.000YYModerateY189.0000.486129.061.0Elevated75.017.250HIGH
1138rKiV278903/09/2020F44.064.4001.76020.800100.600129/78132.049.000145.000YYModerateN176.000NaN129.078.0Elevated53.015.250INTERMEDIARY
1141ejaQ6145October 07, 2025F32.055.1001.77017.600109.700119/61204.068.000137.000NNHighN177.000NaN119.061.0Normal106.013.550LOW
1144ahGL831809-24-2023F37.054.5001.65020.00083.10097/75113.033.00076.000YNModerateY165.0000.50497.075.0Normal50.011.110INTERMEDIARY
1155pgnn467916/01/2023F52.099.3571.89831.59792.526142/95240.049.000164.000YNHighY189.7540.488142.095.0Hypertension Stage 2161.018.219LOW
1169FnQr5343November 20, 2024M33.066.8551.93029.58771.373144/76215.082.000196.000NNModerateN192.9830.370144.076.0Hypertension Stage 2103.017.417LOW